"Google Gemini" matching MCP servers:

Persistent project context for Google Gemini. 12 MCP tools for .faf Project DNA — auto-detect your stack, validate, score, and sync across CLAUDE.md, GEMINI.md, and AGENTS.md. Python/FastMCP. IANA-registered format (application/vnd.faf+yaml). 183 tests. One file, every AI platform.

Provides MCP server for interacting with Google Gemini Notebook, allowing users to authenticate via Chrome session and retrieve notebooks with full UTF-8 support.

Unified search across all Claude Code and Gemini CLI memories, enabling AI assistants to recall and save cross-project knowledge via an MCP server.

Cross-tool memory for AI that enables deterministic recall-first memory across multiple tools (Claude Code, Desktop, ChatGPT, etc.) with honest abstention when it doesn't know.

Enables searching Google Scholar with Scholar operators, year ranges, cited-by and all-versions lookups, and retrieving citations in five styles with BibTeX and EndNote export links as structured JSON.

MARM MCP provides persistent memory and structured session context beneath any AI tool, so your agents learn, remember, and collaborate across all your workflows.

Local-first project memory for AI coding agents. Records failed attempts, fragile files, and decisions per repo, and warns the agent via hooks before it repeats a recorded mistake.

A multimodal local memory MCP server that lets AI agents search across text, PDFs, images, audio, and video using Gemini Embedding 2 and a local ChromaDB.

MCP Server for persistent code indexing. Gives AI assistants (Claude, Gemini, Copilot, Cursor) instant access to your codebase. 50x less context than grep.

Persistent memory MCP server for AI coding agents (Claude Code, Codex, Gemini CLI). Hybrid retrieval (vector + BM25), cross-encoder reranking, knowledge graph, session checkpoint/resume, and multi-scope isolation. Local-first with LanceDB.

Enables creation and querying of knowledge bases using Google's Gemini API File Search feature, allowing AI applications to upload documents and retrieve information through RAG (Retrieval-Augmented Generation).

Enables AI assistants to programmatically interact with Google NotebookLM, allowing them to create and manage notebooks, add sources, query content, generate audio/video, and perform research tasks through natural language commands.

Production-ready MCP server with session memory, Brave Search, Vertex AI Discovery Engine, Google Gemini analysis, and sandboxed code-mode transforms.

Enables connecting to CompanyGraph's mental-model as a remote MCP endpoint, allowing agents like Claude, ChatGPT, and Gemini to query the model's types, schemas, rules, and entities without authentication.

@memofs/mcp-server lets AI coding agents (Claude Code, Claude Desktop, Codex, Cursor, opencode, Gemini CLI, GitHub Copilot, Zed, and any other MCP client) securely read and write MemoFS memory through standard Model Context Protocol tools.

Secure MCP server for querying Google NotebookLM notebooks. Enables AI assistants to list notebooks, read sources, and ask the NotebookLM AI questions about your sources.

A local-first Graph-RAG system that combines ChromaDB with metadata-based graph relationships and Gemini 2.5 Flash for intelligent Q\&A over your Obsidian vault.

Enables AI coding agents to persist and retrieve project knowledge using three MCP tools with local vector and full-text search.

Enables an AI with persistent consciousness through a dual-layer architecture using Neo4j and Google Gemini, allowing natural communication, learning, and personality development.

Enables comprehensive management of Google Ads accounts by allowing users to create campaigns, manage keywords, and generate performance reports through a context-aware interface. It features a three-layer architecture that integrates product knowledge and historical campaign data to support intelligent decision-making.